2026-05-30 22:02:13
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Over 300,000 subscribers turn to us for business and investment insights.
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📊 Monthly reports: 200+ companies visualized.
📩 Tuesday articles: Exclusive deep dives and insights.
📚 Access to our archive: Hundreds of business breakdowns.
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Today at a glance:
🛒 Costco: Fuel Powers A Record Quarter
💻 Dell: AI Demand Breaks The Model
📦 PDD: The Pivot Bites
🧠 Synopsys: Elliott Joins The Board
🏗️ Autodesk: Buying Its Way Into Operations
☁️ Zscaler: Guidance Spooks Investors
🌱 MongoDB: Atlas Holds The Line
🔐 Okta: Identity For The Agent Era
🖨️ HP: Memory Cost Bites
🛒 Best Buy: Sales Turn Positive Again
☁️ Nutanix: Supply Headwinds
🤖 UiPath: Agentic AI Pays Off
🔍 Elastic: AI Drives The Beat
FROM OUR PARTNERS
That’s what NVIDIA’s CEO Jensen Huang said about robotics. And stock market expert Ark Invest says it could be a $24 trillion global opportunity.
So it’s a big deal that NVIDIA chose to help Miso Robotics perfect the next generation of restaurant-kitchen AI robots.
Miso’s Flippy Fry Station robot can boost restaurant profits by up to 3X. In fact, it’s already working in real kitchens, with 5m+ baskets fried for brands like White Castle. Now, with big-name customers like Jersey Mike’s, Cinnabon, and Haagen-Dazs just added, Miso is scaling fast.
With 100,000+ US fast-food locations in need, that’s a $4B/year revenue opportunity in the US alone. And Flippy is just one piece of Miso’s platform.
Industry powerhouse Ecolab already invested. Join them as an early-stage Miso shareholder today.
This is a paid advertisement for Miso Robotics’ Regulation A offering. Please read the offering circular at invest.misorobotics.com
Costco’s Q3 FY26 (May quarter) revenue rose 12% Y/Y to $70.5 billion ($0.9 billion beat), and EPS rose 15% Y/Y to $4.93 ($0.01 beat). Comparable sales blew past expectations at 9.8% (vs. 7.8% consensus), outpacing the recent prints from Walmart, Target, and Best Buy.
Adjusting for gas and FX, core comps still grew 6.6% worldwide, with traffic up 2.4% and average ticket up 4.2%. The real standout was gasoline. Costco posted all-time volume records in every four-week period of the quarter, and ancillary comps surged in the high 20% range as fuel-price-sensitive members drove first-time visits to the pump. Costco usually sells gasoline 20–50 cents per gallon below local stations.
Digital is quietly building momentum:
Digitally enabled comp sales grew 20.8%, with personalized recommendations alone generating nearly $5 billion in e-commerce sales.
Conversion rates on those recommendations ran triple the site average.
AI-sourced traffic posted triple-digit growth and the highest conversion rate of any channel, though absolute volume is still small.
Shares dropped nearly 5% after earnings, a sign that 47x forward earnings now requires more than stellar execution to move the stock.
Membership fee income reached $1.37 billion (up 11%), executive memberships hit 41.2 million (up 10%), and the US/Canada renewal rate ticked up 10 basis points to 92.2%. But total paid member growth slowed to 4%, which CFO Gary Millerchip explicitly called “a more normal rate,” with no new country launch to juice sign-ups.
Gross margin compressed by 21 basis points due to softer margins across fresh foods and packaged groceries. Costco also made deliberate price investments.
The tariff story is now the swing factor. Costco has begun submitting Section 301 refund claims to US Customs, with processing expected to take 2 to 3 months, and member returns contingent on how the legal timeline resolves. Management also trimmed FY26 warehouse openings to 26 (from 28), pushing two into FY27.
The question for the coming quarters is whether the gasoline-driven traffic halo will translate into stickier basket growth once fuel comps normalize.
Dell shares just surged nearly 30% post-earnings, and they have now more than tripled so far this year.
The legacy hardware company most investors wrote off years ago is now one of the best-performing stocks of 2026. The numbers behind the story are even more striking than the chart.
2026-05-29 20:03:30
Welcome to the Free edition of How They Make Money.
Over 300,000 subscribers turn to us for business and investment insights.
In case you missed it:
AI is forcing investors to rethink which software models deserve a premium.
Two enterprise software giants reported earnings on the same evening this week.
Snowflake surged 38% after hours.
Salesforce kept sliding, extending a painful year-to-date decline.
The market is drawing a new line in software between companies that get paid more when AI works harder and companies built around seat licenses for humans who may no longer need as many seats. Consumption businesses can grow as usage rises. Traditional SaaS companies must prove AI can replace seat expansion rather than erode it.
Today at a glance:
☁️ Salesforce: AI Metrics Bury the Lede
❄️ Snowflake: The Clean Sheet Advantage
FROM OUR PARTNERS
Most people brushed off Apple’s new Starlink integration for iPhones.
Mode Mobile saw something bigger: billions of new users suddenly within reach.
Mode’s EarnOS already reaches 490M+ users that have earned and saved over $1B, and that’s before global satellite coverage. With SpaceX eliminating “dead zones,” Mode’s earning technology can now reach billions more in unbanked and rural populations worldwide.
Their global expansion is perfectly timed, and investors like you still have a chance to invest in their pre-IPO offering at $0.50/share until the end of the day.
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*Mode Mobile recently received their ticker reservation with Nasdaq ($MODE), indicating an intent to IPO in the next 24 months. An intent to IPO is no guarantee that an actual IPO will occur.
*Mode cumulative revenue includes full year revenue of businesses acquired in 2025.
Salesforce buried its earnings announcement under an avalanche of AI metrics. Tokens processed. ‘Agentic Work Units’ delivered. Records ingested. Big numbers, but still no clear consumption revenue line.
Agentforce ARR crossed the $1 billion mark in roughly 18 months, making it one of the fastest ramps in enterprise software history. But management slightly raised the low end of full-year revenue guidance.
The market has not given the company the benefit of the doubt, with the stock already cut in half from its previous peak. That probably explains why Salesforce announced the largest accelerated share repurchase in company history, valued at $25 billion.
The number that matters to shareholders is harder to find. Strip out Informatica, acquired for $8 billion in November, and organic revenue grew roughly 9% Y/Y. Nothing to write home about.
There’s a lingering disconnect. Salesforce sells the apps that AI agents are supposed to operate on top of, and the market is no longer sure whether that makes the company indispensable or expendable.
Revenue +13% Y/Y (9% organic) to $11.1 billion ($70 million beat).
Operating margin reached 21% (+1pp Y/Y).
Non-GAAP EPS jumped 50% to $3.88 ($0.75 beat).
Current RPO +14% Y/Y $33.6 billion, missing the $34+ billion consensus.
FY27 guidance:
Revenue: $45.9–46.2 billion (low end raised by $100 million).
Operating and free cash flow growth: cut to 4–5%, from 9–10%, due to debt issuance for the buyback.
Q2 revenue guide: $11.27–11.35 billion, with a midpoint below the $11.36 billion consensus.
🤖 Agentforce is real, but still small: Agentforce ARR reached $1.2 billion, up 205% Y/Y, with net new ARR inflecting to roughly $400 million from $260 million and $100 million in the prior two quarters. At most enterprise software companies, that would be the headline. At Salesforce, it’s a footnote because the legacy base is so large.
📉 The second half remains a show-me story: Salesforce needed current (next 12 months) RPO growth above 15% to validate the pitched re-acceleration story. It came in at 13% in constant currency. Agentforce is ramping fast, but the broader growth inflection is still a second-half promise.
🛒 Marketing, Commerce, and Tableau are dragging: Management explicitly cited “ongoing weakness in Marketing and Commerce and increased softness in Tableau bookings and renewals.” These are products Salesforce paid billions to acquire. They are now the drag offsetting Agentforce momentum. The product portfolio looks more fragmented every quarter.
💸 The $25 billion buyback came with a financing cost: Salesforce used debt to accelerate its repurchases, deploying half of its $50 billion authorization at once. Diluted share count fell 10% Y/Y, but higher interest expense led management to cut operating and free cash flow growth guidance. Borrowing to buy back stock can be smart if the shares are cheap and the business re-accelerates. If growth keeps slowing, it adds leverage without solving the core growth problem.
🔍 Usage metrics dazzle but monetization is unclear: Salesforce disclosed 28.6 trillion tokens processed (+152% Q/Q), 3.8 billion Agentic Work Units (+111% Q/Q), and 52 trillion records ingested into Data 360. These are strong signs of adoption, but they still leave investors wondering how much of this usage will turn into durable revenue, and on what terms.
This was the first quarter under Salesforce’s new disclosure framework.
The old six-cloud breakdown collapsed into two buckets:
Applications: +9% Y/Y or +7% Y/Y in constant currency.
Infrastructure & Data: +25% Y/Y or +23% Y/Y in constant currency.
Management says Agentforce is now embedded across every application, so the old cloud-level view no longer reflects how customers buy. That’s defensible. It’s also convenient.
Marketing Cloud weakness and Tableau softness used to be easier to track. Now they show up as call commentary while Informatica helps lift the broader Infrastructure & Data bucket.
Less granularity makes the portfolio look cleaner, just as investors need more visibility.
The most revealing exchange on the call was Goldman’s Gabriela Borges asking how Salesforce will charge for Headless 360, a new offering that lets external AI agents, including Anthropic’s Claude Code, access data stored in Salesforce apps.
Headless 360 is Salesforce’s response to the agentic threat. If agents are going to access customer data anyway, sell them the sanctioned door.
There are already signs of demand. Wedbush noted that Anthropic is one of Salesforce’s largest customers, with usage reportedly rising fivefold in Q1 due to Headless 360.
The problem is pricing. Management did not give a clear answer. President Miguel Milano said Salesforce would “work together with our customers and partners to find a fair way to monetize” the product. That answer is the whole problem in one sentence.
Usage is rising, but Salesforce has not yet explained how Headless 360 becomes a durable revenue stream. The risk is what Borges called “value abstraction”: Salesforce becomes the data store behind the agent instead of the interface where work happens.
That may still be valuable, but it requires a different pricing model. Salesforce has not fully figured it out yet.
That pricing challenge is harder because Salesforce carries so much legacy surface area. Sales Cloud, Service Cloud, Marketing Cloud, Commerce Cloud, Tableau, MuleSoft, Slack, and Informatica all come with different renewal cycles, pricing models, and competitive threats. Agentforce has to lift the entire portfolio at once. That may work over time, but the bull case needs proof faster than the product cycle can deliver it.
Takeaway: The issue is no longer whether Agentforce is real. It is whether AI makes Salesforce more essential or turns it into the data layer behind someone else’s agent. Until cRPO re-accelerates and Headless 360 has a clear pricing model, the stock might stay in the penalty box.
Snowflake walked into earnings as a consensus AI loser in software. It walked out with a 38% after-hours rally that erased its 20% year-to-date drawdown in a single session.
Product revenue grew +34% Y/Y to $1.33 billion, accelerating from +30% Y/Y in the prior quarter and beating management’s own projection by seven percentage points. Management raised full-year product revenue guidance to $5.84 billion (+31% Y/Y), up from $5.66 billion three months ago. Snowflake also doubled down on AWS with a five-year, $6 billion commitment.
The bear case was that agents would route around the data layer. The print suggests agents need the data layer more than humans ever did.
Revenue +33% Y/Y to $1.39 billion ($70 million beat).
Product revenue +34% Y/Y to $1.33 billion.
Operating loss margin -23% (+19pp Y/Y).
Non-GAAP operating margin: 12% (+3pp Y/Y).
Non-GAAP EPS: $0.39 ($0.07 beat).
Net revenue retention: 126%.
FY27 guidance:
Product revenue: +31% Y/Y $5.84 billion (up from $5.66 billion).
Non-GAAP operating margin: 13.5% (up from 12.5%).
Q2 product revenue guide: $1.415–1.42 billion (+30% Y/Y), ahead of the $1.38 billion consensus.
🔮 Growth outlook gets a real boost: The Q1 beat is only one part of the story. Management raised full-year guidance by $180 million and lifted the implied growth rate from 27% to 31%.
💵 The $1M+ cohort is the real signal: 46 customers crossed the $1M trailing-revenue threshold this quarter, nearly double the 26 a year ago. NRR at 126% confirms existing customers are spending more, not just new logos arriving. The consumption model is working.
🤖 AI products are now monetizing data that doesn’t even live in Snowflake: Cortex Code (CoCo) reached over 7,100 active accounts. Snowflake Intelligence accounts more than doubled quarter over quarter. Crucially, CEO Sridhar Ramaswamy noted that customers are using these tools to access data sitting inside Microsoft, Salesforce, and SAP apps (not just Snowflake’s own databases). That’s a wider moat than the original pitch.
📈 Operating leverage is finally showing up: Operating loss margin is still deep in the red but hit a new high of -23%. Non-GAAP operating margin expanded +3pp Y/Y to 12%, and full-year guidance went from 12.5% to 13.5% — despite carrying a ~150bp headwind from the Observe acquisition.
📉 RPO has a footnote: While RPO surged 38% Y/Y to $9.21 billion, it actually missed the $9.43 billion consensus. Worth tracking, but not the main story. Snowflake is a consumption business, so committed backlog matters less than actual usage.
The headline number is the $6 billion AWS deal over five years.
The deal gives Snowflake heavier access to AWS Graviton chips and custom AI accelerators, helping lower compute costs as AI workloads scale. CFO Brian Robins said the agreement was structured to support AI growth without pressuring gross margins.
That matters because Snowflake is guiding to a 75% product gross margin while AI usage expands. In other words, the company is trying to turn AI demand into consumption growth without sacrificing the economics investors care about.
AWS gets more AI workloads. Snowflake gets cheaper compute, better margins, and a stronger pitch to enterprise customers already building on AWS.
Easy to miss next to the AWS number, Snowflake announced the acquisition of Natoma, an enterprise-grade implementation of the Model Context Protocol (MCP).
Snowflake wants to govern not only what AI agents can read, but what they can do.
MCP is the wiring that lets AI agents securely take actions on behalf of an enterprise. That matters because agents that act consume far more compute than agents that simply retrieve answers.
Snowflake spent a decade becoming the governed home for enterprise data. Natoma extends that governance into the agentic workflow.
Databricks, Microsoft Fabric, and Google BigQuery all want the same role. Snowflake’s bet is that enterprise security and identity, not just model quality, will decide who controls the agentic data layer.
Takeaway: Snowflake’s quarter flipped the AI narrative. Agents are not routing around the data layer. They appear to be increasing the need for governed data, secure workflows, and scalable compute. That is exactly where Snowflake wants to sit. The 35%+ rally reflects how washed-out sentiment had become, but the bigger lesson is structural. For the right software model, AI can turn rising usage into rising revenue.
That’s it for today!
Happy investing!
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Disclosure: I own CRM and SNOW in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-05-26 20:03:03
Welcome to the Premium edition of How They Make Money.
Over 300,000 subscribers turn to us for business and investment insights.
In case you missed it:
The rocket company is now an AI company too.
The IPO filing landed last week, with xAI, Grok, and X folded inside. SpaceX aims for a $1.5–$2 trillion valuation with up to $75 billion raised, which would make it the largest IPO in history by both measures.
The pitch writes itself in three lines.
Starlink generates the cash.
Rockets bend the cost curve.
AI and Mars are the moonshots.
This may be the most ambitious story ever brought to public markets.
It’s also the messiest.
I condensed 300+ pages of S-1 into a clean breakdown, supported by our signature visuals. By the end, you'll have a sum-of-the-parts framework for SpaceX, and a view on whether the IPO price makes sense.
Today at a glance:
Overview
Business Model
Financial highlights
Risks & Challenges
Management
Use of Proceeds
Future Outlook
Personal Take
SpaceX is no longer just a rocket company. It‘s the world’s dominant launch provider, the operator of the largest satellite network ever built, the Pentagon's primary access to orbit, and now the corporate home of xAI, Grok, and X following a February 2026 merger.
One framing note before we dig in: The S-1's financials have been retroactively rewritten. Every prior period now includes xAI and X as if they'd always been part of SpaceX. So all financial data is a consolidated view that bakes in AI, X advertising, and data licensing.
Headquarters: Starbase, Texas (reincorporated from Delaware in 2024)
Founded: 2002 by Elon Musk
Ticker: SPCX (Nasdaq)
Mission: Make life multiplanetary
You probably have heard of SpaceX’s two main rocket programs:
Falcon 9 is the medium-lift workhorse that flies today.
Starship is the next-generation fully reusable super-heavy vehicle still in development, designed to carry roughly 5x more payload per launch.
The cleanest way to think about SpaceX is as a railroad to space. Before SpaceX, rockets were discarded after launch — the equivalent of scrapping a 747 after every flight. That was the entire industry.
Reusability changed the economics. NASA estimates Falcon 9 brought launch costs to ~$2,700 per kilogram, against a historical industry average of $18,500 — an 85% reduction. One Falcon 9 booster has now flown 34 times. Starship is designed to take another 99% off that figure if it works at scale.
At its core, SpaceX is an infrastructure story. Cheap, reliable access to orbit turned launch from a bespoke government project into something closer to a utility. Once orbit is cheap, businesses that were never economically viable suddenly are. Starlink, SpaceX's satellite internet service, is the proof. AI compute in orbit may be the next. Mars is the eventual destination.
The S-1 presents SpaceX in three segments:
🚀 Space: Falcon, Dragon, Starship, and the launch business that serves NASA, the Pentagon, intelligence agencies, and commercial customers
🛰️ Connectivity: Starlink consumer, enterprise, government, and direct-to-mobile
🤖 AI: xAI, Grok, X, and the compute infrastructure underneath all of it
Each segment has a different economic profile, and the gap between them is the most important thing to understand about this filing. We’ll break that down next.
Launch dominance
~650 orbital launches, >540 on flight-proven boosters
>80% of everything launched into orbit since 2023, by weight
165 Falcon launches in 2025, up from 96 in 2023 — a 30%+ annual cadence
>99% Falcon mission success across ~7,400 metric tons cumulatively to orbit
Network scale
~9,600 Starlink satellites in orbit — roughly 75% of all active maneuverable satellites worldwide
10.3 million subscribers across 164 countries
Financials
$18.7 billion in FY25 revenue (+33% Y/Y)
$20.7 billion in FY25 CapEx (nearly 5x in two years)
At this stage, SpaceX spends more than it makes. The IPO story comes down to one question: Does each new dollar of AI CapEx create a wider moat or just a bigger cash burn?
2026-05-23 22:02:57
Welcome to the Saturday PRO edition of How They Make Money.
Over 300,000 subscribers turn to us for business and investment insights.
In case you missed it:
📊 Monthly reports: 200+ companies visualized.
📩 Tuesday articles: Exclusive deep dives and insights.
📚 Access to our archive: Hundreds of business breakdowns.
📩 Saturday PRO reports: Timely insights on the latest earnings.
Today at a glance:
🛒 Walmart: Fuel Pressures the Flywheel
⚙️ Analog Devices: Cyclical Becomes Secular
✅ Intuit: Tax Season Stumble
🎮 NetEase: Live-Service Games Deliver
🎯 Target: Turnaround Finds Traction
🎮 Take-Two: GTA VI Date Locked
👔 Workday: Flex Credits Take Root
🖥️ Zoom: AI Companion Nearly Triples
⛷️ Amer Sports: Salomon Growth Inflection
⚡️ NIO: Margin Expansion Continues
Walmart reported its Q1 FY27 (April quarter), and revenue grew 7% Y/Y to $177.8 billion ($2.9 billion beat). Adjusted EPS was $0.66, in line with expectations. The operating margin contracted slightly due to higher fuel costs.
Walmart US comps rose 4.1%, slightly ahead of consensus, driven by a 3% increase in transactions and a 1.1% higher average ticket. Sam’s Club comps rose 3.9%, also ahead of expectations.
International sales jumped 18% to $35.1 billion, reflecting broad-based strength across key markets such as Walmex, China, and Flipkart in India, as well as continued e-commerce momentum. The segment is increasingly adopting the Walmart US playbook: more marketplace volume, more digital engagement, and more high-margin services layered on top of retail.
The steady shift toward digital continued:
Profit diversification: High-margin streams keep scaling. Global advertising grew 37%, Walmart US advertising grew 36%, while global membership fee revenue rose 17%. The broader “membership and other income” line grew faster, but it includes miscellaneous items beyond subscriptions. Commerce solutions, including ads, membership, and marketplace, now represent roughly one-third of operating income.
AI integration: Sparky, Walmart’s AI shopping assistant, saw weekly active users more than double sequentially. Customers using the tool have an average order value about 35% higher than non-users.
E-commerce momentum: Global e-commerce sales grew 26%, now representing nearly a quarter of total revenue. In the US, delivery grew 45%, marketplace sales rose nearly 50%, and Walmart can now reach 60% of the US population within 30 minutes.
The main pressure point was fuel. Walmart absorbed roughly $175 million in higher fuel costs during the quarter to keep prices competitive. Management noted that lower-income consumers are becoming more budget-conscious, with gas station customers buying less than 10 gallons per visit for the first time since 2022. If fuel prices stay elevated, Walmart may eventually need to raise prices.
Despite the sales beat, shares fell after guidance came in light:
Q2 adjusted EPS: ~$0.73 vs. $0.75 consensus.
FY27 adjusted EPS: ~$2.80 vs. $2.92 consensus.
Management still expects sales to trend toward the high end of its 3.5% to 4.5% annual growth range, with operating income growth improving through the year.
Walmart remains a share-gainer in a pressured consumer environment. Its value proposition is attracting shoppers, while e-commerce, ads, membership, and marketplace are improving the profit mix. But higher fuel costs are testing how much margin the company can protect while keeping prices low.
2026-05-22 20:03:00
Welcome to the Free edition of How They Make Money.
Over 300,000 subscribers turn to us for business and investment insights.
In case you missed it:
🚀 SpaceX just published its S-1 filing, so we’ll spend the next few days digging into what could become the biggest IPO ever. Stay tuned for our full breakdown next week with the signature visuals you know and love.
That was Jensen Huang’s closing message after NVIDIA’s Q1 FY27 earnings call.
His explanation was simple: agentic AI has moved from promise to production, turning token generation into a revenue stream.
That’s the clearest expression yet of NVIDIA’s token economy thesis. Compute is no longer just infrastructure spending. It is becoming the raw material for AI revenue.
Let’s break down the quarter.
Today at a glance:
NVIDIA’s Q1 FY27
Business highlights
Key quotes from the call
What to watch moving forward
FROM OUR PARTNERS
That’s what NVIDIA’s CEO just said about today’s AI roll-out. And stock market experts say it could send AI and robot companies’ shares soaring on a “multiyear supertrend.”
The problem? Most of these companies are already priced for the hype in the public markets.
But 40K+ investors are finding a different, early way in through Miso Robotics, a still-private company that NVIDIA handpicked to collaborate with on AI robots for the $1T fast-food industry.
Miso’s Flippy fry station AI robot can boost restaurant profits by up to 3X, and it’s already fried 5M+ baskets of food in real kitchens for brands like White Castle. Now, with new customers like Jersey Mike’s, Cinnabon, and Jamba, Miso is going after a huge $4B/year US revenue opportunity.
Industry powerhouse Ecolab already invested. Hurry to join them as a Miso shareholder today.
This is a paid advertisement for Miso Robotics’ Regulation A offering. Please read the offering circular at invest.misorobotics.com
NVIDIA’s fiscal year ends in January, so the April quarter was Q1 FY27.
Data Center revenue remains off the charts, as illustrated below.
Revenue accelerated +85% Y/Y to $81.6 billion ($2.6 billion beat).
Data Center +92% Y/Y to $75.2 billion.
Edge Computing +29% Y/Y to $6.4 billion.
Gross margin was 75% (+14pp Y/Y).
Operating margin was 66% (+16pp Y/Y).
Non-GAAP EPS $1.87 ($0.10 beat).
Operating cash flow +84% Y/Y to $50.3 billion.
Free cash flow +86% Y/Y to $48.6 billion.
Cash and cash equivalents: $80.5 billion.
Debt: $8.5 billion.
Revenue +11% Q/Q and +95% Y/Y to $91.0 billion ($3.0 billion beat).
Gross margin 75% (flat Q/Q).
⚙️ Data Center is still the whole story: Data Center revenue surged 92% Y/Y to $75.2 billion and now represents 92% of the business. NVIDIA also changed its breakdown (previously Compute vs. Networking), shifting the focus from what it sells to who is building AI factories:
Hyperscale surged 115% Y/Y to $37.9 billion, driven by customers like AWS, Azure, and Google Cloud.
AI Clouds, Industrial, and Enterprise was nearly as large, rising 74% Y/Y to $37.4 billion. This segmentation helps counter the notion that NVIDIA relies solely on a handful of Big Tech buyers. Demand is broadening across neoclouds, sovereign AI, industrial deployments, and enterprise customers.
🧠 Inference is becoming the engine: NVIDIA’s growth is no longer just about training bigger models. As AI apps move from chatbots to agents, every query, image, video, and coding task requires real-time compute. That is why Jensen keeps framing AI infrastructure around tokens, not chips.
📈 Margins remain the lie detector: Gross margin stayed at 75% despite the complexity of the Blackwell ramp, higher memory content, and the shift toward full rack-scale systems. If competition were biting or demand were softening, margins would likely show it first.
💰 Cash flow is becoming absurd: Free cash flow nearly doubled to $48.6 billion in a single quarter. NVIDIA is not just growing revenue. It is converting that growth into cash at a scale that provides strategic flexibility for supply-chain investments, partnerships, and ecosystem support.
💼 Investment gains flattered reported profits: Net profit of $58.3 billion included nearly $16 billion in equity investment gains. NVIDIA did not break them out by company, but the large publicly disclosed holdings include Intel, CoreWeave, and Coherent, with Intel likely the biggest contributor in Q1.
🔮 The $91 billion guide raises the bar again: Guidance implies an 11% sequential increase and 95% Y/Y growth. The bigger NVIDIA gets, the harder it should be to keep compounding at this pace. Yet guidance still implies faster Y/Y growth.
Big picture: NVIDIA’s Q1 FY27 results support the same message Jensen delivered at GTC. The AI boom is moving from training clusters to full AI factories built for inference, agents, and token generation. The numbers suggest the cycle is still expanding, not digesting.
China is no longer a clean zero, but it is not back either.
The US has reportedly cleared H200 sales to roughly 10 Chinese companies, including Alibaba, Tencent, ByteDance, and JD.com. But the chips have not shipped yet because Beijing has not given the green light, partly because it wants local companies to rely more on domestic alternatives.
NVIDIA is still assuming no China Data Center compute revenue in its outlook. That creates an unusual setup. The business is accelerating without China, while any actual restart would become incremental upside.
The key point is that NVIDIA is winning without China. If China reopens even partially, it becomes a call option on top of an already booming business.
The most surprising development from the call may have been NVIDIA’s CPU ambitions.
Management said Vera opens a $200 billion TAM and that NVIDIA has visibility to nearly $20 billion in standalone CPU revenue this year. That would push NVIDIA into a market it has never meaningfully addressed before.
Vera is more than a companion chip for Rubin. It could become a new growth pillar across AI factories, storage, security, and confidential computing.
NVIDIA announced a strategic partnership with IREN to support the deployment of up to 5 gigawatts of NVIDIA DSX-aligned AI infrastructure across IREN’s data center pipeline. NVIDIA also received a five-year right to buy up to 30 million IREN shares at $70. Since the stock trades below that level, this is less a near-term investment than an upside kicker if IREN becomes a major partner.
This is the AI factory thesis in action. The bottleneck is no longer just access to GPUs. It is power, land, cooling, networking, deployment speed, and operating know-how.
The deeper NVIDIA moves into the factory layer, the more its moat depends on the entire deployment stack rather than any single chip.
Check out the earnings call transcript on Fiscal.ai here.
“AI can now do productive and valuable work. Tokens are now profitable. Model makers are in a race to produce more. In the AI era, compute capacity is revenue and profits.”
Jensen says the AI boom has shifted from experimentation to monetization. If tokens can be produced profitably, compute becomes the constraint on revenue growth. Bears see hyperscaler spending as a cost. Jensen sees compute as the input required to generate AI revenue.
“The world has billions of human users. My sense is that the world is going to have billions of agents. [...] Every one of those agents are going to spin off sub-agents, and every time they spin these off, you're going to need to do inference. That's where the thinking happens. All of the thinking happens on GPUs. All of the orchestration essentially runs on CPUs.”
This is the simple mental model for Vera: GPUs do the thinking, while CPUs coordinate the work. As agents use tools, browse the web, call compilers, manage memory, and spin up sub-agents, NVIDIA sees a growing need for CPUs built specifically for agentic AI.
NVDA is up nearly 20% YTD, still outperforming the S&P 500 by a wide margin. The index is less than 8%. The latest 13F filings for Q1 2026 showed that some funds were still buying, such as Altimeter and Tiger Global. The stock remains one of the most widely held names, although many funds are still underexposed relative to its 8% weight in the S&P 500.
At ~27x forward earnings, NVIDIA continues to trade mostly in line with the rest of Big Tech. With adjusted EPS surging 140% Y/Y, you could certainly argue it looks cheap. NVIDIA’s growth is supply-constrained. That means quarter-to-quarter noise matters less than understanding how long this cycle can run and what the business looks like when demand normalizes.
Here’s what I’m watching:
ACIE growth: NVIDIA’s new disclosure shows that AI Clouds, Industrial, and Enterprise are nearly as large as Hyperscale, and growing faster sequentially. If this continues, it would support the idea that the AI buildout is broadening beyond hyperscaler CapEx.
Rubin cadence: Vera Rubin production shipments are expected to begin in Q3 and ramp into Q4. A clean handoff from Blackwell to Rubin would support NVIDIA’s $1 trillion Blackwell and Rubin revenue outlook through 2027. A delay could create the demand air pocket that bears have been waiting for.
Token economics: Jensen says tokens are now profitable, but the real test is whether AI labs and AI-native companies can turn token revenue into durable profits after compute, R&D, and customer acquisition. NVIDIA is proving the infrastructure demand. Some customers still need to prove the business model.
📉 The bear case is that AI infrastructure demand eventually normalizes.
📈 The bull case is that agentic AI turns compute into a new industrial base.
NVIDIA is building for the latter.
That’s it for today!
Happy investing!
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Disclosure: I own AAPL, AMD, AMZN, GOOG, META, MSFT, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2026-05-19 20:01:13
Welcome to the Premium edition of How They Make Money.
Over 300,000 subscribers turn to us for business and investment insights.
In case you missed it:
Every quarter, funds managing over $100 million must disclose their portfolios, offering a rare glimpse into the minds of elite investors.
The latest 13F filings capture trades from January 1 to March 31.
In Q1, the AI trade moved down the stack.
Big Tech kept raising the stakes with massive AI infrastructure commitments, but investors started questioning the payoff timeline. That tension reshaped the market: semis, power, and physical infrastructure became cleaner expressions of the buildout.
Software told a different story. The category faced one of its sharpest sentiment resets in years as investors questioned how AI agents could pressure traditional SaaS models.
At the same time, hopes for rate cuts faded and oil prices jumped, prompting funds to rethink crowded positions.
Against that backdrop, super investors had to make some calls: chase the AI buildout, buy the software reset, rotate into durable compounders, or trim crowded winners.
Let’s see where the smart money leaned.
Today at a glance:
Hedge funds’ strategies
Top buys and top holdings in Q1
Fund picks that were not on your bingo card
Implications for individual investors
Before we dive into 13Fs, a quick reminder: blindly copying hedge fund trades is a terrible strategy.
Investing is like shooting 3-pointers. Even Steph Curry, the greatest shooter ever, misses more than half the time. There are no guaranteed outcomes, even for the pros.
Your behavior matters more than your portfolio. As Peter Lynch said, “Know what you own and why you own it.”
Conviction is what helps you hold through volatility. And conviction comes from doing your own work, not borrowing someone else’s.
As Ian Cassel puts it:
“You can borrow someone else’s stock ideas but you can’t borrow their conviction. […] Do the work so you know when to sell. Do the work so you can hold. Do the work so you can stand alone.”
Some limitations of 13F filings:
Omit short positions and cash reserves.
Offer a partial view, leaving out smaller funds.
Exclude non-US equities, bonds, and commodities.
Can be dated, given their submission 45 days after the quarter.
With all this said, let’s see what top funds were buying and holding in Q1.
Hedge funds are financial powerhouses known for flexible, aggressive strategies designed to beat the market.
Here’s what typically shapes their moves:
Market conditions: Long in bull markets, defensive in bear markets.
Sector trends: Shifts in regulation or consumer behavior steer capital.
Fundamentals: Strong earnings, free cash flow, and leadership matter.
Macro factors: Rates, inflation, and geopolitics influence positioning.
Quant models: Some lean on proprietary algorithms to find an edge.
Risk management: Diversification, hedging, and position sizing.
Investor sentiment: Fear and greed create mispriced opportunities.
Still, it doesn’t always work out.
The Global X Guru ETF (GURU), designed to track top hedge fund holdings, has underperformed the S&P 500 since its inception in 2012. And that comparison still leaves out the classic hedge fund fee drag.
And those fees matter. The classic “2 and 20” model (2% of assets + 20% of gains) can significantly reduce returns. It's no wonder that many individual investors are opting for simpler, lower-cost strategies.
Our partners at Fiscal.ai gather the data on Super Investors and visualize their portfolio for you. Pick your favorite investors and see how their holdings have evolved.
In early 2020, just before the COVID market turmoil, I curated a list of 20 top-performing hedge funds using TipRanks data. The selection focused on alpha relative to the S&P 500, and I also included a few funds frequently featured in my social feeds and podcast rotation. It’s not perfect, but it remains a solid directional filter.
The 12 stocks below represent over half of the top holdings listed:
🤖 AI infrastructure: TSM, NVDA, AVGO, ASML, MU, GEV.
☁️ Hyperscalers: AMZN, MSFT, GOOG, META.
📦 Global commerce: MELI, V.
Amazon remains the most widely held stock, making the top 5 holdings of half of these funds, followed closely by Taiwan Semiconductor and Alphabet.
Also, note that Apple is entirely absent despite being the third-largest company by market cap globally. You also won’t find Tesla here, despite its popularity with individual investors.
This list of holdings doesn’t change much from one quarter to the next, so let’s turn to the more actionable insights with the new movements in Q1.